A major challenge facing geophysical science today is the unavailability of high-level analysis tools with which to study the massive amount of data produced by sensors or long simulations of climate models. We have developed a prototype information system called QUEST to provide content-based access to massive datasets. QUEST employs workstations as well as teraFLOP computers to analyze geoscience data to produce spatial-temporal features that can be used as high-level indexes. Our first application area is global change climate modeling. In the initial prototype, the first features extracted are cyclones trajectories from the output of multi-year climate simulations produced by a General Circulation Model. We present an algorithm for cyclone extraction and illustrate the use of cyclone indexes to access subsets of GCM data for further analysis and visualization.<>


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Extracting spatio-temporal patterns from geoscience datasets


    Contributors:
    Mesrobian, E. (author) / Muntz, R.R. (author) / Santos, J.R. (author) / Shek, E.C. (author) / Mechoso, C.R. (author) / Farrara, J.D. (author) / Stolorz, P. (author)


    Publication date :

    1994-01-01


    Size :

    1979494 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Extracting Spatio-Temporal Patterns from Geoscience Datasets

    Mesrobian, E. / Muntz, R. R. / Mechoso, C. R. et al. | British Library Conference Proceedings | 1994


    Optimizing Tertiary Storage Organization and Access for Spatio-Temporal Datasets

    Ling Tony Chen / Rotem, D. / Shoshani, A. et al. | British Library Conference Proceedings | 1995



    Mining regular crime patterns in spatio-temporal databases

    Kumar, G. Vijay / Kumar, N. Dilip / Sai, R. Lakshmi Prasanna | IEEE | 2017


    Learning spatio-temporal patterns for predicting object behaviour

    Sumpter, N. / Bulpitt, A. | British Library Online Contents | 2000